Checklist the per-video bar — engine/sim
82.4/100
quantitative quality · weights learn from your reviews (
engine.sim.memory review thread-minus-the-hype-W42-Mon good|bad)⚠ 6 flag(s) — not yet ship-ready: copy_genericlow_vo_coveragedead_airgeneric_scene · see docs/strategy/VIDEO-CHECKLIST.md
Composition comp · template family · expected output
9:16 Reelrendered1:1 Squarepending16:9 Widepending9:16 4Kpending1:1 4Kpending16:9 4KpendingGIF (SMS)pending
expected output: 1/7 rendered — same matrix the /media preview surfaces for this asset.
Composition layer × scene 5 scenes · 29.2s · comp_id + rendered still + tier + the script
| # | Layer (comp_id · still · tier) | Beat | Timecode | Motion | Logo | Audio | VO / on-screen / caption |
|---|---|---|---|---|---|---|---|
| 1 |  matches intent shared field signature-3d | hook | 0–5.6s | kinetic-build | icon·liquid-chrome | ♪ node_lock | The reflex everyone has: my output is weak, so I need the most expensive model. on-screen: "Bigger model fixes everything" |
expected on screen: red ground · knot hero in the shared Signal Field · Magister leads · mark · kinetic-build · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual markshape knotground redtreatment liquid-chromemotion kinetic-buildpower laser-lockinstrument laser-trace→"Bigger model fixes everything" | |||||||
| 2 |  matches intent JsonDiff template | diff | 5.6–13.399999999999999s | crossfade-8f | icon·white-knockout | ♪ node_lock | What we really do: small fast models do the easy jobs. The big model is saved for the hard thinking. on-screen: Reality: route by task |
expected on screen: red ground · a JsonDiff panel over a dimmed Signal Field · Magister leads · code · crossfade-8f · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id JsonDiffvisual codeshape knotground redtreatment white-knockoutmotion crossfade-8fpower morphinstrument morph→Reality: route by taskcurate codeLines, fileName, lines | |||||||
| 3 |  matches intent ChecklistCard template | teach | 13.4–19.2s | kinetic-build | icon·liquid-chrome | ♪ node_lock | The real cost of defaulting to the biggest model is a tenfold bill for output you'd have gotten anyway. on-screen: The cost: tenfold bill, same output |
expected on screen: red ground · a ChecklistCard panel over a dimmed Signal Field · Magister leads · node-graph · kinetic-build · icon·liquid-chrome logo · caption bottom-left spec (the prompt): comp_id ChecklistCardvisual node-graphshape knotground redtreatment liquid-chromemotion kinetic-buildpower morphinstrument morph+laser→The cost: tenfold bill, same outputcurate items, nodes | |||||||
| 4 |  first render · fix pending StatScoreboard templatedead_airgeneric_scene | proof | 19.2–24.2s | receipts-count | icon·white-knockout | ♪ node_lock | Minus the Hype |
expected on screen: red ground · a StatScoreboard panel over a dimmed Signal Field · Magister leads · receipts · receipts-count · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id StatScoreboardvisual receiptsshape knotground redtreatment white-knockoutmotion receipts-countpower receiptsinstrument spotlight→Minus the Hypecurate pillar, stats, statsLabels | |||||||
| 5 |  first render · fix pending shared field signature-3ddead_airgeneric_scene | resolve | 24.2–29.2s | coalescence | icon·white-knockout | ♪ bed_out | Minus the Hype |
expected on screen: red ground · knot hero in the shared Signal Field · Magister leads · coalescence · coalescence · icon·white-knockout logo · caption bottom-left spec (the prompt): comp_id shared Signal Fieldvisual coalescenceshape knotground redtreatment white-knockoutmotion coalescencepower coalescenceinstrument coalescence→Minus the Hype | |||||||
Format stack 3 aspects · same scenes[], re-cropped
9:16
1080×1920
Stories · TikTok · YouTube Shorts · Reels
1:1
1080×1080
LinkedIn · Facebook · Instagram
16:9
1920×1080
X/Twitter · YouTube · LinkedIn video
Channels 9 destinations
LinkedInX/TwitterYouTubeInstagramFacebookThreadsTikTokPinterestBluesky
Social captions supplemental published copy · per channel (comp_id level)
tiktokYour output is weak so you reach for the most expensive model. Wrong fix. We run small fast models for the easy jobs and save the big one for the hard thinking. Same result, a tenth of the bill. (AI-assisted)
instagramThe reflex: bigger model fixes everything.
The move: route by task.
Small fast models do extraction and sorting.
The big model only handles the hard reasoning.
Same output. A tenth of the cost.
#AItools #aiworkflow #builtnotbought #automation #costcontrol
linkedinMost teams default to the most expensive model when their output looks weak. The bill goes up tenfold and the output does not change.
What we actually do: route by task. Small fast models handle extraction and classification. The big model only gets the hard reasoning that needs it.
The takeaway: pick the model per job, not per habit. You get the same answer for a fraction of the spend. We run our own pipeline this way.
x"Bigger model fixes everything" is a reflex, not a strategy. Route by task: small fast models for easy jobs, the big one for hard reasoning. Same output, a tenth of the bill. Method here:
facebookWhen your AI output looks weak, the reflex is to reach for the most expensive model. The bill jumps tenfold and the answer stays the same.
The real move is to route by task. Use small fast models for the easy jobs and save the big model for the hard thinking. Try sorting your tasks into easy and hard before you pick a model.
threadsEveryone has the same reflex: my output is weak, so I need the biggest model.
That is a tenfold bill for output you would have gotten anyway.
Route by task instead. Small fast models for easy jobs. Big model only for the hard reasoning. Same answer, way less spend.
pinterestHow to cut AI costs without losing quality: route by task. Use small fast models for extraction and classification, and reserve the expensive large model only for hard reasoning. Model routing strategy for cheaper AI workflows and lower API bills.
blueskyThe reflex: my output is weak, so I need the biggest model. The result: a tenfold bill for the same answer. Route by task instead. Small models for easy jobs, big model for hard reasoning.
youtubeTitle: Stop Paying ten times for AI. Route by Task Instead.
The reflex is to reach for the most expensive model when output looks weak. That is a tenfold bill for the same answer. We route by task: small fast models for extraction and classification, the big model only for hard reasoning. This short shows the method so you can sort your own tasks and pick the right model per job. (AI-assisted)